James Kermode
James Kermode
Professor of Materials Modelling, School of Engineering, University of Warwick
E-mail megerősítve itt: warwick.ac.uk - Kezdőlap
Hivatkozott rá
Hivatkozott rá
The atomic simulation environment—a Python library for working with atoms
AH Larsen, JJ Mortensen, J Blomqvist, IE Castelli, R Christensen, ...
Journal of Physics: Condensed Matter 29 (27), 273002, 2017
Machine learning unifies the modeling of materials and molecules
AP Bartók, S De, C Poelking, N Bernstein, JR Kermode, G Csányi, ...
Science advances 3 (12), e1701816, 2017
Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
Z Li, JR Kermode, A De Vita
Physical review letters 114 (9), 096405, 2015
Machine learning a general-purpose interatomic potential for silicon
AP Bartók, J Kermode, N Bernstein, G Csányi
Physical Review X 8 (4), 041048, 2018
Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum
O Barrera, D Bombac, Y Chen, TD Daff, E Galindo-Nava, P Gong, D Haley, ...
Journal of materials science 53 (9), 6251-6290, 2018
Low-speed fracture instabilities in a brittle crystal
JR Kermode, T Albaret, D Sherman, N Bernstein, P Gumbsch, MC Payne, ...
Nature 455 (7217), 1224-1227, 2008
Hybrid atomistic simulation methods for materials systems
N Bernstein, JR Kermode, G Csanyi
Reports on Progress in Physics 72 (2), 026501, 2009
Atomistic aspects of fracture
E Bitzek, JR Kermode, P Gumbsch
International Journal of Fracture 191, 13-30, 2015
A universal preconditioner for simulating condensed phase materials
D Packwood, J Kermode, L Mones, N Bernstein, J Woolley, N Gould, ...
The Journal of Chemical Physics 144 (16), 2016
In situ stable crack growth at the micron scale
G Sernicola, T Giovannini, P Patel, JR Kermode, DS Balint, TB Britton, ...
Nature Communications 8 (1), 108, 2017
Macroscopic scattering of cracks initiated at single impurity atoms
JR Kermode, L Ben-Bashat, F Atrash, JJ Cilliers, D Sherman, A De Vita
Nature communications 4 (1), 2441, 2013
A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers
M Caccin, Z Li, JR Kermode, A De Vita
International Journal of Quantum Chemistry 115 (16), 1129-1139, 2015
f90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codes
JR Kermode
Journal of Physics: Condensed Matter 32 (30), 305901, 2020
Expressive programming for computational physics in Fortran 950+
G Csányi, S Winfield, J Kermode, MC Payne, A Comisso, A De Vita, ...
Newsletter of the Computational Physics Group, 1-24, 2007
Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W
AM Goryaeva, J Dérès, C Lapointe, P Grigorev, TD Swinburne, ...
Physical Review Materials 5 (10), 103803, 2021
Dissociative Chemisorption of Inducing Stress Corrosion Cracking in Silicon Crystals
A Gleizer, G Peralta, JR Kermode, A De Vita, D Sherman
Physical Review Letters 112 (11), 115501, 2014
A first principles based polarizable O (N) interatomic force field for bulk silica
JR Kermode, S Cereda, P Tangney, A De Vita
The Journal of chemical physics 133 (9), 2010
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials
B Onat, C Ortner, JR Kermode
The Journal of Chemical Physics 153 (14), 2020
Development of an exchange–correlation functional with uncertainty quantification capabilities for density functional theory
M Aldegunde, JR Kermode, N Zabaras
Journal of computational physics 311, 173-195, 2016
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
L Zhang, B Onat, G Dusson, A McSloy, G Anand, RJ Maurer, C Ortner, ...
Npj Computational Materials 8 (1), 158, 2022
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